Vehicle navigation systems can use object detection and classification to identify obstacles that may or may not come into the path of the vehicle. For autonomous vehicle (AV) navigation, these identified objects can be placed into a high resolution three-dimensional (3D) model of the surroundings of the vehicle. The 3D model is formed by defining a 3D grid of spaces and tracking points entering and exiting the spaces to understand what space is free, filled, or unknown due to sensor occlusion.
For example, when an object moves behind another object the system may reasonably conclude that the space behind the occlusion is occupied by that object and not free space. As the object exits the occluded area, the system may reasonably conclude that the occluded space is now free or at least that it is vacated by the object. The object classification is helpful for predicting the behavior of objects and for making navigation decisions to avoid obstacles.
Lidar, for example, can be used to generate a dense 3D point cloud and to track the movement of objects as points in that cloud. The movement of objects is tracked in all three dimensions so that lidar data is particularly well suited to generating a high resolution 3D model of the surroundings. Objects in a lidar point cloud can be identified by the lidar or in some other way. For example, visible and NIR (Near Infrared) cameras can classify objects based on color boundaries and patterns. These edges and patterns can be compared with known patterns to detect and classify objects.